To increase sales, merchants and service providers use a variety of styles and channels for advertising to potential customers. The decision whether to spend the money on advertising is often not the major question. Rather, merchants need to decide how their advertising money will be most effective in reaching receptive and purchase prone segments of the population. It therefore makes sense that a merchant or other advertiser would want to spend their advertising dollars in the most effective manner possible. This means that they will need to target their advertising efforts at those consumers who are most likely to make a purchase in response to receiving or viewing an advertisement. This type of advertising is known as target marketing.
Traditional target marketing relies on consumer surveys and tracking spending patterns for online and brick-and-mortar sales on a merchant-by-merchant basis. Although such targeting can be somewhat helpful, there are major deficiencies in this type of target marketing. For example, the consumer surveys are often self-selecting in that only certain types of consumer are likely to respond to print, telephone or in-person consumer surveys. These types of consumers can fall into many categories, but tend to be self-sort based on the type of survey that is being conducted.
Print surveys, like the type included on a self-addressed postage-paid postcard along with a purchased product, require that a consumer take the time to fill-in the survey and then send it back. Such surveys automatically exclude busy, lazy and/or indifferent consumers and target only certain personality types that will take the time necessary to respond. Similarly, consumers are sometimes asked to visit a website printed on a paper or online/email receipt after completing a purchase to participate in a survey. Such tactics can have similar results in only capturing a small portion of potential consumers.
To improve the response rate of print or online consumer surveys, some merchants and advertiser have taken to offering incentives likes rebates, discounts and special offers to consumers who respond to the survey. However, these programs can skew consumer responses if the consumer thinks his or her response will have an impact on the size or amount of the promised incentive. Furthermore, incentive programs often result in capturing responses from only extremely cost sensitive consumers and exclude the most valuable type of consumers who are likely to pay full retail prices.
On the other end of the spectrum of consumer surveys are person-to-person surveys, such as telephone and in-person surveys, in which an interviewer asks a consumer to respond to a set of questions. These types of surveys are even more effective in polarizing consumers. Consumers who are likely to respond person-to-person surveys are a very small subset of all potential consumers. Most consumers simply do not have the time or interest to participate in person-to-person surveys and some are in fact very annoyed by such intrusions on their time. As such, most types of consumer surveys have significant limitations on how well they can provide information for targeted marketing based on the likelihood that only limited portions of the potential consumer population will respond, and do so accurately.
To get the best information as to the interests and propensity for spending, some online advertisers have taken to detecting key words and phrases in consumers' browser-based email activity and web browsing to determine what consumers in a particular areas determined by IP addresses might find appealing and likely to purchase. Although such techniques are more passive, in that consumers do not have to enter actively any information in addition to the information they are already entering to perform intended functions or complete other online tasks, the feedback loop on the effectiveness of such advertising is still rather limited.
Although key-word and IP address based advertising can track the click-through rate of consumers who are selectively chosen to be shown a particular advertisement, that information can really only be used to determine the effectiveness of the algorithms used to determine which consumers should be shown a particular advertisement and not the effectiveness of the advertisement in obtaining a sale or potential for a particular sale based on past or trending purchasing behavior on a population on the consumer level.
Some advertiser rely on the merchants themselves and other data mining operations to track spending and sale patterns of consumers on a merchant-by-merchant basis. While useful, such data is limited in a variety of ways. In scenarios in which spending patterns are determined by looking at a merchant's own customers' spending, that information is really only useful for incentivizing existing customers to spend more and not particularly helpful in finding new customers. Data mining of particular market segments looking for potential new customer information has similar limitations. Most merchants will want to protect their customers' information from being used by competitors to lure customers away. Because of the competitive nature of most markets, data reported by most merchants tends to be scrubbed of any useful information that could possibility be used to measure the effectiveness of a particular advertising campaign or promotional program in obtaining new customers.
Advertisers, publishers and merchants need new, accurate and cost-effective ways to target and track the effectiveness of print and online advertising. Embodiments of the present invention address this and other needs.